Inductive logic programming at 30
, 2021 Inductive logic programming (ILP) is a form of logic-based machine learning. The goal of ILP is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge.
Cropper, Andrew +3 more
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Knowledge discovery in variant databases using inductive logic programming. [PDF]
Bioinform Biol Insights, 2013 Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions.
Nguyen H, Luu TD, Poch O, Thompson JD.
europepmc +3 more sources
Discovering rules for protein-ligand specificity using support vector inductive logic programming. [PDF]
Protein Eng Des Sel, 2009 Kelley LA +3 more
europepmc +3 more sources
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. [PDF]
BMC Bioinformatics, 2012 Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically
A Santos JC +4 more
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A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models. [PDF]
BMC Bioinformatics, 2011 Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions.
Bernardes JS, Carbone A, Zaverucha G.
europepmc +2 more sources
Rule Learning over Knowledge Graphs: A Review [PDF]
Transactions on Graph Data and Knowledge, 2023 Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks.
Wu, Hong +4 more
doaj +1 more source
Learning and reasoning with graph data. [PDF]
Front Artif Intell, 2023 Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks.
Jaeger M.
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المنطق الماصدقي: تاريخه وخصائصه وتطبيقاته [PDF]
Maǧallaẗ Kulliyyaẗ Al-ādāb Ǧāmiʿaẗ Būrsaʿīd, 2022 لم يُعرف التمييز بين حدي القضية - المفهوم والماصدق - بشکلٍ انفصالي کلٌ على حدة إلاَّ في وقتٍ متأخر؛ فکل قضية تتکون من حدين هما المفهوم والماصدق، والعلاقة بينهما عکسية کما نعلم؛ کلما زاد المفهوم قل الماصدق والعکس، لکن هذا لا يعني القول بأحدهما فقط دون ...
محمد سيد محمد أبوالعلا
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Extending Coinductive Logic Programming with Co-Facts [PDF]
Electronic Proceedings in Theoretical Computer Science, 2017 We introduce a generalized logic programming paradigm where programs, consisting of facts and rules with the usual syntax, can be enriched by co-facts, which syntactically resemble facts but have a special meaning.
Davide Ancona +2 more
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Program Logics for Homogeneous Generative Run-Time Meta-Programming [PDF]
Logical Methods in Computer Science, 2015 This paper provides the first program logic for homogeneous generative run-time meta-programming---using a variant of MiniML by Davies and Pfenning as its underlying meta-programming language.
Martin Berger, Laurence Tratt
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